Application performance is one of the components that contribute to overall software quality. As such, different types of diagnostic tools have been developed to help diagnose, analyze and improve application performance. Most of these tools collect extensive information about the application performance for pre-production usage in development, test, or staging environments and include application code profilers, stress load simulators, database profilers and others. Unfortunately, however while pre-production performance analysis is crucial, it typically does not address all performance related issues. For example, consider the situation of a typical modern application that consists of multiple application components interacting with each other. Two factors that may contribute to the performance of each application component include the execution time of the application component code itself and the time spent on resource requests for external application components and systems, such as relational databases, LDAP resources, web services and others.
The execution time of the component code typically has a predictable dependency on the execution environment and the application state and as such, typically may be optimized in the development and/or the test environment. On the other hand, the resource request execution time varies greatly depending upon the resource state and the application environment. In fact, in many cases it is very hard or nearly impossible to emulate a real production state on a test platform or a staging platform. One example of such a situation involves a database driven system with a high volume of data that is too large and/or expensive to replicate on a test platform. Other examples involve software applications that rely on external Web services, wherein the external Web services are not controlled by the development team and as a result, cannot be properly stress tested.
For these types of situations, it is desirable to monitor and collect information relating to the performance of the application in the production environment. The production environment puts specific requirements on monitoring tools. One of these requirements involves a proper balance between the performance information collected and the overhead introduced by a monitoring tool. For this reason, a monitoring tool should be able collect enough information to facilitate discovery of a performance problem's root cause while introducing a minimal amount of disturbance to the production system. Another requirement involves the ability to detect application performance problems at the application action level and the ability to correlate the application performance with heavy resource requests to provide the root cause for application performance degradation.